# Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

@article{Locatello2019ChallengingCA, title={Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations}, author={Francesco Locatello and Stefan Bauer and Mario Lucic and Sylvain Gelly and Bernhard Sch{\"o}lkopf and Olivier Bachem}, journal={ArXiv}, year={2019}, volume={abs/1811.12359} }

The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. [... ] Key Method We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Expand

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